Method and apparatus for selecting targeted components in limited access test

ABSTRACT

A system that can test individual components having tolerances on a circuit board without complete access to every node on the board is disclosed. The system uses a method that develops test limits from a model of the board, component tolerances, and a list of accessible nodes. A method of reducing the complexity of the test problem by limiting the number of components under consideration is also disclosed. A method of reducing the complexity of the test problem by limiting the number of nodes under consideration is also disclosed. A method of picking nodes to apply stimulus to a board is also disclosed. Finally, a method of correcting for certain parasitics associated with tester hardware is disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

A number of related copending United States patent applications commonlyowned by the assignee of the present document and incorporated byreference in their entirety into this document are being filed in theUnited States Patent and Trademark Office on or about Oct. 9, 1998. Thelist of these applications is as follows: Hewlett Packard Company docketnumber 10971074-1, entitled “METHOD AND APPARATUS FOR LIMITED ACCESSCIRCUIT TEST”; Hewlett Packard Company docket number 10971075-1,entitled “METHOD AND APPARATUS FOR SELECTING STIMULUS LOCATIONS DURINGLIMITED ACCESS CIRCUIT TEST”; Hewlett Packard Company docket number10971081-1, entitled “METHOD AND APPARATUS FOR CANCELING ERRORS INDUCEDBY THE MEASUREMENT SYSTEM DURING CIRCUIT TEST”; Hewlett Packard Companydocket number 10982227-1, entitled “METHOD AND APPARATUS FOR SELECTINGTEST POINT NODES FOR LIMITED ACCESS CIRCUIT TEST”; Hewlett PackardCompany docket number 10982228-1, entitled “METHOD AND APPARATUS FORCORRECTING FOR DETECTOR INACCURACIES IN LIMITED ACCESS TESTING”; and,Hewlett Packard Company docket number 10971078-1, entitled “METHOD ANDAPPARATUS FOR BOARD MODEL CORRECTION.”

FIELD OF THE INVENTION

This invention relates generally to circuit board testing. Moreparticularly, this invention relates to the identification ofmanufacturing defects and faulty components on a circuit board.

BACKGROUND OF THE INVENTION

Generally, a circuit board consists of numerous interconnectedcomponents such as semiconductor chips, resistors, capacitors,inductors, etc. After circuit boards have been assembled, but beforethey can be used or placed into assembled products, they must be tested.Testing verifies that the proper components have been used, that eachcomponent performs within test limits, that all required electricalconnections have been properly completed, and that all necessaryelectrical components have been attached to the board in the properposition and with the proper orientation. When a component is notperforming within test limits, it is said to be faulty.

A common way to test assembled printed circuit boards is calledin-circuit test. In-circuit testing involves probing individual boardcomponents through a so-called “bed-of-nails” and verifying theirexistence and specifications independent of surrounding circuitry. Awell known series of circuit board testing machines for in-circuittesting is the Hewlett-Packard Company Model HP-3070 Family of CircuitBoard Testers. The HP-3070 Family of board testers are fully describedin the HP-3070 Family Operating and Service Manuals available fromHewlett-Packard Company. Other families of circuit board testingmachines made by Hewlett-Packard are the HP-3060 and HP-3065 series.

To test each individual board component, in-circuit testing requiresaccess to every node on the circuit board. With through-hole parts,access is directly available at component leads. With surface mountparts, access is provided through vias and test pads that are placed onthe circuit board when it is designed. Increases in board density,however, have led to a decrease in the size of vias that has eclipsedthe ability of probe technology to contact a smaller target. Vias noware often one hundred times smaller in area than vias used just a fewyears ago. Furthermore, test pads that are large enough to be probedsuccessfully require a substantial amount of board area that wouldotherwise be used to place and connect components. Therefore, on manycircuit boards it is no longer practical, or desirable, to probe everynode on the board.

Accordingly, there is a need in the art for a test technique andapparatus that can test individual circuit board components havingtolerances without requiring access to every node on the circuit board.Such a technique should be generalized so that it can be used with manydifferent circuits and tolerance ranges. Furthermore, it is desirablethat such a system be implemented on existing in-circuit testinghardware to preserve existing capital and process investments in thathardware.

SUMMARY OF THE INVENTION

In a preferred embodiment, the invention selects targeted componentsfrom a larger group of components with inaccessible nodes. By selectinga subset of the larger group of components the complexity of the testproblem is reduced. Furthermore, the invention also provides possiblestimulus locations for testing the group of selected components. Theinvention is generally applicable to all kinds of circuits and may beimplemented using existing computer and tester hardware.

The invention constructs a topology graph of the larger group ofcomponents. Then an inaccessible node is selected. This node is used asthe starting point for a recursive traversal of the topology graph. Therecursion of the topology graph stops at accessible nodes, and descendsat inaccessible nodes. The nodes stopped at are possible stimuluslocations. The devices traversed are the selected devices. In thismanner, the selected devices are “surrounded” by accessible nodes thatmay be measured, or have stimulus applied to them.

After being stored, the selected devices are then removed from thetopology graph. The process may then be repeated to select another groupof targeted components. This can continue until there are no moreinaccessible nodes.

Other aspects and advantages of the present invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the major components involved inlimited access testing consistent with an embodiment of the presentinvention.

FIG. 2 is a flowchart illustrating generally the major steps taken bythe test program generator consistent with an embodiment of the presentinvention.

FIG. 3 is a schematic diagram illustrating a circuit that may be modeledby a Simplified Tableau consistent with an embodiment of the presentinvention.

FIG. 4 is a schematic diagram of a circuit that is used to helpillustrate a preferred embodiment consistent with an embodiment of thepresent invention.

FIG. 5 is a plot of the change in voltage as a single component in thecircuit of FIG. 3 is swept through a range of values consistent with anembodiment of the present invention.

FIG. 6 is a flow chart illustrating the steps to optimize the objectivefunction used to find test limits consistent with an embodiment of thepresent invention.

FIG. 7 is a schematic diagram of an example reduced cluster consistentwith an embodiment of the present invention.

FIGS. 8 and 9 are schematic diagrams of the reduced cluster of FIG. 7showing stimulus in different locations consistent with an embodiment ofthe present invention.

FIG. 10 is a flowchart illustrating a process of generating a figure ofmerit for various stimulus consistent with an embodiment of the presentinvention.

FIG. 11A is a diagram illustrating the non-ideal properties of themeasurement and stimulus hardware consistent with an embodiment of thepresent invention.

FIG. 11B is a diagram illustrating the non-ideal properties of themeasurement and stimulus hardware with the stimulus being replaced byit's thevenin equivalent consistent with an embodiment of the presentinvention.

FIG. 12 is a flowchart illustrating a process for generating clustersfrom a board topology consistent with an embodiment of the presentinvention.

FIG. 13 is a flowchart that illustrates the process for generatingreduced clusters from a cluster consistent with an embodiment of thepresent invention.

FIG. 14 is a flowchart illustrating the steps taken to perform nodepruning for a reduced cluster consistent with an embodiment of thepresent invention.

FIG. 15 is a flowchart that illustrates a process that corrects fornon-ideal properties of the measurement and stimulus hardware consistentwith an embodiment of the present invention.

FIG. 16 is a diagram that illustrates a topology graph consistent withan embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1, a database 102 that may be stored on a storagedevice coupled to a computer is used to feed a model of the circuit tobe tested to a test program generator 104. The model contains theinformation necessary to test the board including component types,component interconnections, component values, component tolerances, andan indication of which circuit nodes are accessible. The test programgenerator creates a test program that controls the operation of themeasurement hardware 106 as it applies stimulus to, and takesmeasurements on, the circuit under test 110. The results of thesemeasurements, and information provided by the test program generator 104are used by a fault analysis routine 108 to produce pass/fail anddiagnostic information on the circuit under test. The test programgenerator and the fault analysis routine may be programs or routinesthat are executed on a computer.

The test program generator 104 or fault analysis routine 108 may bestored in memory on a computer, stored on disk, or any other computerreadable medium. They may be part of a single piece of executable code,or they may be separate programs, or routines. Furthermore, they may beexecuted on the same computer, or they may be run on different pieces ofhardware. The hardware implementing the test system shown in FIG. 1 maybe a general purpose computing device coupled to the measurementhardware and executing executable code or it may include customhardware, such as an application specific integrated circuit thatintegrates one or more of the functions shown.

FIG. 2 illustrates the major steps taken by the test program generator108 consistent with an embodiment of the present invention. In a step202, the test program generator reads the circuit model and any otherinput data that is necessary, such as which nodes are accessible and thecomponent tolerances. In a step 204, dangling and shorted components areremoved from the model. Dangling components are those that cannot betested because there is no path between any of the accessible nodes thatpasses through that component. In a step 206 the circuit model is brokendown into groups of components that are electrically isolated. Thesegroups of components are called “clusters.” A cluster is defined as agroup of components that for test purposes is connected to the remainderof the circuitry by zero or one node. In a step 208 each cluster isfurther broken down into reduced clusters and the nodes needed to testeach reduced cluster are pruned. A reduced cluster is a group oftargeted components that reduces the size and complexity of the testgeneration, measurement, and fault analysis problems in succeedingstages of the test process. The process of generating “reduced” or“immediate” clusters and selecting the “test points” for each reducedcluster is described in more detail in another part of this document.

In a step 210, stimulus and measurements nodes are selected to test eachcomponent in each reduced cluster. Some stimulus and measurement nodesmay be selected to test more than one component. The stimulus andmeasurements may be optimized for test throughput, test coverage, orsome tradeoff between the two.

In a step 212, for each stimulus, corresponding measurement nodes, anddesired number of simultaneous faults to be tested, at least oneequivalence class, U* matrix, and set of test limits are generated. Theterms equivalence class, U* matrix, etc. will be discussed in furtherdetail later. For now, suffice it to say that these terms represent theinformation that will be used later by the fault analysis routine toprovide pass/fail and diagnostic information.

In a preferred embodiment, the process of generating the equivalenceclasses, U* matrices, and test limits for each stimulus andcorresponding measurement nodes operates on a reduced cluster. Thisprocess, however, can be applied to much larger groups of components,including the whole board.

As with many testing methodologies, a model of the test circuit mustfirst be constructed. This may be the whole board, or some smallernumber of components, such as, for example, a cluster. Likewise, it mayinvolve more than one stimulus and any number of measurement nodes.However, for simplicity and computational efficiency, a preferredembodiment uses a test circuit model that involves the components ofonly one reduced cluster and the stimulus that is being applied by thetester.

The test circuit model contains information on the tester stimulus, thecomponents, and their interconnection so that the testing methodologyhas enough information to tell when a component is faulty. Many possiblecircuit models and formulations exist. Many of these models andformulations are described in Computer Methods for Circuit Analysis andDesign by Jiri Vlach and Kishore Singhal, Van Nostrand ReinholdPublishing, New York, N.Y., 1983 and Computer-Aided Analysis ofElectronic Circuits, Algorithms and Computational Techniques by Leon O.Chua and Pen-Min Lin, Prentice-Hall, Englewood Cliffs, N.J., 1975 whichare both hereby incorporated herein by reference.

In a preferred embodiment, a Simplified Tableau model will be used. TheSimplified Tableau model is given by Equation 1. $\begin{matrix}{{\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}\begin{bmatrix}I_{b} \\V_{n}\end{bmatrix}} = \begin{bmatrix}S \\0\end{bmatrix}} & ( {{EQN}.\quad 1} )\end{matrix}$

Where I_(b) is a column vector representing the branch currents, V_(n)is a column vector representing the node voltages, A is the reducedincidence matrix, A^(T) is the transpose of the reduced incidencematrix, K_(i) and K_(v) are matrices derived from the ranch constitutiveequations, and S is the source vector that contains the value of theindependent sources. Usually the independent sources are a result of thestimulus applied by the tester hardware. For the purposes of thisdiscussion, n (non-subscripted) is chosen to represent the number ofnodes in the circuit and b (non-subscripted) is chosen to represent thenumber of branches.

The rules for constructing K_(i) and K_(v) are straightforward andgenerally involve setting each element of the K_(i) and K_(v) matricesaccording to a table that relates the type of circuit element (i.e.impedance, admittance, current source, current controlled voltagesource, etc.) and its value to the appropriate entries in K_(i) andK_(v).

To illustrate how K_(i) and K_(v) may be constructed, examine FIG. 3.FIG. 3 shows a simple circuit with n=3 nodes and b=4 branches consistentwith an embodiment of the present invention. This circuit may be theentire board, a cluster, or just the component of a reduced cluster withthe tester applied stimulus. I_(b) will have four currents I₁-I₄ andV_(n) will have two node voltages (V_(n1) and V_(n2)). Note that thereare n−1 node voltages in the V_(n) vector instead of n node voltagesbecause the node voltages are relative to a reference node. Thereference node (usually ground) is often assigned the number 0. The nodevoltage of the reference node, in FIG. 3, V_(n0), is by definition zeroand, as such, need not be included as one of the node voltages.

K_(i), also known as the impedance matrix, for the network of FIG. 3 canbe: $\begin{matrix}{K_{i} = \begin{bmatrix}1 & \quad & \quad & \quad \\\quad & R_{2} & \quad & \quad \\\quad & \quad & 1 & \quad \\\quad & \quad & \quad & 1\end{bmatrix}} & ( {{EQN}.\quad 2} )\end{matrix}$

K_(v), also known as the admittance matrix, for the network of FIG. 3and the K_(i) of Equation 2 is: $\begin{matrix}{K_{v} = \begin{bmatrix}0 & \quad & \quad & \quad \\\quad & 1 & \quad & \quad \\\quad & \quad & {sC}_{3} & \quad \\\quad & \quad & \quad & G_{4}\end{bmatrix}} & ( {{EQN}.\quad 3} )\end{matrix}$

where s is the Laplace transform variable. Note that by entering thecapacitance in admittance form when constructing K_(i) and K_(v), the svariable is kept in the numerator. To simplify the mathematics if onlypassive components are present, K_(i) and K_(v) may be constructed suchthat K_(i) is the identity matrix. This is shown in Equations 4 and 5 asfollows: $\begin{matrix}{K_{i} = \begin{bmatrix}1 & \quad & \quad & \quad \\\quad & 1 & \quad & \quad \\\quad & \quad & 1 & \quad \\\quad & \quad & \quad & 1\end{bmatrix}} & ( {{EQN}.\quad 4} ) \\{K_{v} = \begin{bmatrix}0 & \quad & \quad & \quad \\\quad & \frac{1}{R_{2}} & \quad & \quad \\\quad & \quad & {sC}_{3} & \quad \\\quad & \quad & \quad & G_{4}\end{bmatrix}} & ( {{EQN}.\quad 5} )\end{matrix}$

Finally, the incidence matrix, A, which defines the interconnection ofthe components for FIG. 3 is: $\begin{matrix}{A = \begin{bmatrix}{- 1} & 1 & 1 & 0 \\0 & 0 & {- 1} & 1\end{bmatrix}} & ( {{EQN}.\quad 6} )\end{matrix}$

Note that this incidence matrix was constructed such that a branchcurrent flowing into a node is defined as having a negative sign, and abranch current flowing out of a node is defined as having a positivesign. These definitions could be reversed.

By substituting Equations 4-6 into Equation 1, the Simplified Tableaufor the network shown in FIG. 3 is obtained: $\begin{matrix}{{\begin{bmatrix}1 & \quad & \quad & \quad & 0 & 0 \\\quad & {1\quad} & \quad & \quad & \frac{- 1}{R_{2}} & 0 \\\quad & \quad & 1 & \quad & {- {sC}_{3}} & {sC}_{3} \\\quad & \quad & \quad & 1 & 0 & {- G_{4}} \\\quad & \quad & \quad & \quad & \quad & \quad \\{- 1} & 1 & 1 & {0\quad} & {\quad 0} & {0\quad} \\0 & 0 & {- 1} & 1 & 0 & 0 \\\quad & \quad & \quad & \quad & \quad & \quad\end{bmatrix}\begin{bmatrix}I_{1} \\I_{2} \\I_{3} \\I_{4} \\V_{n1} \\V_{n2}\end{bmatrix}} = \begin{bmatrix}J_{1} \\0 \\0 \\0 \\0 \\0\end{bmatrix}} & ( {{EQN}.\quad 7} )\end{matrix}$

Equation 7 may be solved for a particular value of s to determine thenode voltages and branch currents. Solving Equation 7 can be done byhand using matrix methods, or by any number of computerized mathpackages or methods.

One of the ways a tableau equation can be solved for the branch currents(I_(b)) and node voltages (V_(n)) is to compute the inverse of thetableau. In terms of the generalized case given in Equation 1 this wouldbe: $\begin{matrix}{\begin{bmatrix}I_{b} \\V_{n}\end{bmatrix} = {\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}^{- 1}\begin{bmatrix}S \\0\end{bmatrix}}} & ( {{EQN}.\quad 8} )\end{matrix}$

This inverse can be partitioned such that a submatrix containing onlythose terms that affect the node voltages (V_(n)) can be extracted. Toillustrate: $\begin{matrix}{\begin{bmatrix}I_{b} \\V_{n}\end{bmatrix} = {{\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}^{- 1}\begin{bmatrix}S \\0\end{bmatrix}} = {\begin{bmatrix}\quad & \quad \\Z & \quad\end{bmatrix}\begin{bmatrix}S \\0\end{bmatrix}}}} & ( {{EQN}.\quad 9} )\end{matrix}$

The Z matrix is composed of n−1 rows and b columns where, once again, nis the number of nodes in the circuit, and b is the number of branches.The extraction of the Z matrix allows the use of the following simpleequation to relate node voltages to applied independent sources:

V_(n)=ZS   (EQN. 10)

If each circuit branch in the model has only one component, a propertyof the Z matrix is that its columns have a one-to-one correspondence toeach of the components. This means that composite components, such as aninductor that has both a series resistance and an ideal inductance,should be split into multiple branches to maintain this one-to-onerelationship. (i.e. a branch for the resistance and a branch for theinductance.) For example, if the columns of the incidence matrix, A,correspond to the components in FIG. 3 as follows (note that since eachbranch is a single component, the terms component and branch may be usedinterchangeably.): $\quad \begin{matrix}J_{1} & R_{2} & C_{3} & G_{4}\end{matrix}\quad$ $A = \begin{bmatrix}{- 1} & 1 & 1 & 0 \\0 & 0 & {- 1} & 1\end{bmatrix}$

then the Z matrix, which has the same (n−1)×b dimension as the A matrix,would have the same correspondence between its columns. To illustrate:$\quad \begin{matrix}J_{1} & R_{2} & C_{3} & G_{4}\end{matrix}\quad$ $Z = \lbrack \quad \begin{matrix}{\vdots \quad} & \vdots & {\quad \vdots \quad} & {\quad \vdots} \\\vdots & {\quad \vdots \quad} & {\quad \vdots} & {\quad \vdots}\end{matrix} \rbrack$

The Simplified Tableau model describing any circuit whose componentseach have a single, pre-defined value is given in a generalized way byEquation 1. When component values are changed, (for example, by having atolerance instead of a single value, or by being faulty) the circuit maybe described using this equation: $\begin{matrix}{{\begin{bmatrix}K_{i} & {{- ( {K_{v} + {\Delta \quad K_{v}}} )}A^{T}} \\A & 0\end{bmatrix}\begin{bmatrix}{I_{b} + {\Delta \quad I_{b}}} \\{V_{n} + {\Delta \quad V_{n}}}\end{bmatrix}} = \begin{bmatrix}S \\0\end{bmatrix}} & ( {{EQN}.\quad 11} )\end{matrix}$

Equation 11 shows that a change in the admittance matrix K_(v) by anamount ΔK_(v) results in changes ΔI_(b) and ΔV_(n) in the branch-currentand node-voltage vectors, respectively, for the same stimulus, S. Tosimplify the math, K_(i) is kept constant by opting to describe allchanges in component values as changes in the admittance matrix K_(v).For example, if the nominal value of the resistor in FIG. 3 is R₂ and itincreases from that nominal by 100 Ohms, then: $\begin{matrix}{{\Delta \quad K_{v}} = \begin{bmatrix}0 & \quad & \quad & \quad \\\quad & ( {\frac{1}{R_{2}} - \frac{1}{R_{2} + 100}} ) & \quad & \quad \\\quad & \quad & 0 & \quad \\\quad & \quad & \quad & {\quad 0}\end{bmatrix}} & ( {{EQN}.\quad 12} )\end{matrix}$

In the general case, subtracting Equation 1 from Equation 11 gives:$\begin{matrix}{{\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}\begin{bmatrix}{\Delta \quad I_{b}} \\{\Delta \quad V_{n}}\end{bmatrix}} = \begin{bmatrix}{\Delta \quad K_{v}{A^{T}( {V_{n} + {\Delta \quad V_{n}}} )}} \\0\end{bmatrix}} & ( {{EQN}.\quad 13} )\end{matrix}$

from which: $\begin{matrix}{\begin{bmatrix}{\Delta \quad I_{b}} \\{\Delta \quad V_{n}}\end{bmatrix} = {{\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}^{- 1}\begin{bmatrix}{\Delta \quad K_{v}{A^{T}( {V_{n} + {\Delta \quad V_{n}}} )}} \\0\end{bmatrix}} = {{\begin{bmatrix}\quad & \quad \\Z & \quad\end{bmatrix}\begin{bmatrix}{\Delta \quad K_{v}{A^{T}( {V_{n} + {\Delta \quad V_{n}}} )}} \\0\end{bmatrix}}}}} & ( {{EQN}.\quad 14} )\end{matrix}$

Extracting the parts of Equation 14 that affect ΔV_(n) gives:

ΔV _(n) =ZΔK _(v) A ^(T)(V _(n) +ΔV _(n))   (EQN. 15)

In a preferred embodiment, not all of the nodes of a circuit under testare accessible to the measurement hardware. Therefore, not all of thenode voltages can be measured. The rows of the matrices in Equation 15can be re-arranged and partitioned into changes in node voltages thatcan be observed, and changes that cannot be observed as follows:$\begin{matrix}{\begin{bmatrix}{\Delta \quad V_{({n,{a\quad c}})}} \\{\Delta \quad V_{({n,{nac}})}}\end{bmatrix} = {\begin{bmatrix}Z_{({a\quad c})} \\Z_{({nac})}\end{bmatrix}\Delta \quad K_{v}{A^{T}( {V_{n} + {\Delta \quad V_{n}}} )}}} & ( {{EQN}.\quad 16} )\end{matrix}$

where “ac” designates the set of nodes accessible to the measurementhardware and “nac” designates the non-accessible nodes.

Accordingly, the following equation gives only the observable changes innode voltages:

ΔV _((n,ac)) =Z _((ac)) ΔK _(v) A ^(T)(V _(n) +ΔV _(n))   (EQN. 17)

In a preferred embodiment, the terms of this equation are used togenerate equivalence classes, U* matrices, and test limits. The processfor generating the equivalence classes and U* matrices preferablyfollows the process taken in the conventional T_(F)-Equivalence classapproach. Additional information regarding the T_(F)-Equivalence classapproach can be found in “The T_(F)-Equivalence Class Approach to AnalogFault Diagnosis Problems” by Togawa, Matsumoto, and Arai, IEEETRANSACTIONS ON CIRCUITS AND SYSTEMS, vol. cas-33 no. 10, October 1986.

A rigorous mathematical description of the T_(F)-Eqivalence classapproach is given in the previously mentioned paper by Togawa, The T_(F)-Equivalence Class Approach to Analog Fault Diagnosis Problems, IEEETRANSACTIONS ON CIRCUITS AND SYSTEMS, vol. cas-33 no. 10 (1986) which ishereby incorporated herein by reference. A simple example based on thecircuit shown in FIG. 4 consistent with an embodiment of the presentinvention will be used to illustrate some concepts of theT_(F)-Equivalence class approach. The circuit of FIG. 4 has onlyresistances in addition to a source stimulus. However, the same basicmethods of the T_(F)-Equivalence class approach may be applied whencomponents have complex impedances.

Assume that the components in FIG. 4 have nominal values as shown in thefigure. Also, assume that the only accessible nodes in addition to thereference node V_(n0), are V_(n1) and V_(n2). If each of the componentsis held constant at its nominal value except one, and that one componentis swept through a large range of values, it produces a line whenΔV_(n2) is plotted versus ΔV_(n1). ΔV_(n1) and ΔV_(n2) are the changesin V_(n1) and V_(n2) from their nominal values. When this is done foreach component in turn, the plot of FIG. 5 which is consistent with anembodiment of the present invention results. In FIG. 5, each line islabeled with the component that was being varied.

This plot helps illustrate the equivalence classes of FIG. 4. Take forexample, the case where it is assumed that all components are at theirnominal value except for one. First, V_(n1) and V_(n2) are measured onthe circuit under test to obtain a ΔV_((n1,test)) and ΔV_((n2,test))which are then plotted on FIG. 5. If the location of this point fell onthe lines for R₁, R₂, or R₃ it would indicate that correspondingcomponent is the component that is not at its nominal value. If thelocation of the point fell on the line for R₄ and R₅, then it wouldindicate that either, or both, of R₄ or R₅ is not at its nominal valueand that the rest of the components are at their nominal values.Furthermore, if the point does not lie on any of the lines, then it canbe concluded that some combination of more than one component beingnon-nominal has occurred (other that R₄ and R₅ both being non-nominal).

As shown in FIG. 5, the lines for R₄ and R₅ overlap. In general then, itis impossible to distinguish the case when R₄ is non-nominal from thecase when R₅ is non-nominal by looking only at the voltages at theaccessible nodes V_(n1) and V_(n2). These components are then said to bein the same equivalence class.

The methods of the T_(F)-Equivalence class approach are applicable tohigher dimensions. That is to say, to include larger circuits with moreaccessible nodes, to include complex as well as real voltagesmeasurements, to include multiple components being non-nominal, multiplestimulus, and multiple stimulus frequencies. The foregoing example waslimited to only two dimensions, and one non-nominal component at a time,so that it would be easily visualized and could be shown on atwo-dimensional plot.

In a preferred embodiment, the equivalence classes may be determinedusing the elements of the Z_((ac)) matrix of Equation 17. Theconstruction of the Z_((ac)) matrix and the equivalence classes istypically done by the test program generator 104 based on theinformation obtained from database 102 after partitioning the circuitinto clusters. For the case when only two nodes are accessible (V_(n1)and V_(n2)) the Z_((ac)) matrix for the circuit shown in FIG. 3 is:$\begin{matrix}{Z_{({a\quad c})} = \begin{bmatrix}1000 & 1000 & 500 & 125 & 375 \\0 & 500 & {- 250} & 187.5 & 562.5\end{bmatrix}} & ( {{EQN}.\quad 18} )\end{matrix}$

where Z_((ac)) has a column ordering that corresponds to R₁, R₂, R₃, R₄,and R₅, respectively. A complete list of all the equivalence classes maybe constructed by checking whether each combination of elements andgroups of elements belong to the same equivalence class and thengrouping them accordingly. In general, two groups of components belongto the same equivalence class when it is impossible to distinguish thecase when one group has non-nominal components from when the other grouphas non-nominal components merely by looking at the voltages at theaccessible nodes. In more detail, the following procedure may be used todetermine if two components, or groups of components, belong in the sameequivalence class. The two groups of components are referred to as f1and f2, respectively, and each may be comprised of one or morecomponents being non-nominal.

1. Assemble a matrix from the columns of the Z_((ac)) matrixcorresponding to the first group (Z_((ac,f1))). Calculate its rank,rank(Z_((ac,f1))).

2. Assemble a matrix from the columns of the Z_((ac)) matrixcorresponding to the second group (Z_((ac,f2))). Calculate its rank,rank(Z_((ac,f2))).

3. Assemble a matrix from the columns of the Z_((ac)) matrixcorresponding to the union of the first group and the second group(Z_((ac,f1∪f2))). Calculate its rank, rank(Z_((ac,f1∪f2))).

4. If and only ifrank(Z_((ac,f1))=rank(Z_((ac,f2)))=rank(Z_((ac,f1∪f2))) do the groups f1and f2 belong to the same equivalence class.

Using f1={R₁} and f2={R₂} as an example:${1.\quad Z_{({{a\quad c},{f1}})}} = {{\begin{bmatrix}1000 \\0\end{bmatrix}\quad {{rank}( Z_{({{a\quad c},{f1}})} )}} = 1}$${2.\quad Z_{({{a\quad c},{f2}})}} = {{\begin{bmatrix}1000 \\500\end{bmatrix}\quad {{rank}( Z_{({{a\quad c},{f2}})} )}} = 1}$${3.\quad Z_{({{a\quad c},{{f1} \Cup {f2}}})}} = {{\lbrack {\begin{matrix}1000 \\0\end{matrix}\begin{matrix}1000 \\500\end{matrix}} \rbrack \quad {{rank}( Z_{({{a\quad c},{{f1} \Cup {f2}}})} )}} = 2}$

4. Since rank(Z_((ac,f1∪f2)))≠rank(Z_((ac,f1)))=rank(Z_((ac,f2))) thenR₁ and R₂ do not belong to the same equivalence class.

Another example, this time with f1={R₁,R₄} and f2={R₁,R₅}:${1.\quad Z_{({{a\quad c},{f1}})}} = {{\lbrack {\begin{matrix}1000 \\0\end{matrix}\begin{matrix}125 \\187.5\end{matrix}} \rbrack \quad {{rank}( Z_{({{a\quad c},{f1}})} )}} = 2}$${2.\quad Z_{({{a\quad c},{f2}})}} = {{\lbrack {\begin{matrix}1000 \\0\end{matrix}\begin{matrix}375 \\562.5\end{matrix}} \rbrack \quad {{rank}( Z_{({{a\quad c},{f2}})} )}} = 2}$${3.\quad Z_{({{a\quad c},{{f1} \Cup {f2}}})}} = {{\lbrack {\begin{matrix}1000 \\0\end{matrix}\begin{matrix}125 \\187.5\end{matrix}\begin{matrix}1000 \\0\end{matrix}\begin{matrix}375 \\562.5\end{matrix}} \rbrack \quad {{rank}( Z_{({{a\quad c},{{f1} \Cup {f2}}})} )}} = 2}$

4. Since rank(Z_((ac,f1)))=rank(Z_(ac,f2)))=rank(Z_((ac,f1∪f2))) thegroups f1={R₁,R₄} and f2={R₁,R₅} belong to the same equivalence class.

In a preferred embodiment, a mathematically and computationallyadvantageous method of checking whether a point lies in the space of anequivalence class in an arbitrary number of dimensions is used. Thisallows a set of measurements at the accessible nodes to be used todetermine which equivalence class contains the components or componentsthat are non-nominal for an arbitrary number of accessible nodes.

The procedure to find the equivalence classes (if any) that containnon-nominal components, which is also used by the T_(F)-Equivalenceclass approach, is summarized as follows:

1. Select one member of an equivalence class. For clarity, f1 willrepresent the group of components in the member of the equivalenceclass, and ℑ will represent the equivalence class. Therefore, f1 is amember of ℑ. Assemble a matrix from the columns of the Z_((ac)) matrixcorresponding to the components in this group. (Z_((ac,f1))).

2. Use the Singular Value Decomposition (“SVD”) to factor the matrixfrom step 1 into three terms.SVD(Z_((ac,f1)))=U_((ac,f1))Σ_((ac,f1))W^(T) _((ac,f1)). (Note: the SVDis commonly written as the matrix product of the three terms, U, Σ, andV^(T) where U is the matrix containing the left singular vectors, Σ, isthe matrix containing singular values, and V is the matrix containingthe right singular vectors. Unfortunately, the variable V has beenchosen to mean voltage in the preceding discussions. Therefore, thisdiscussion will use the variable W to represent the right singularmatrix. Also note that the rank of Z_((ac,f1)) is easily obtained fromSVD(Z_((ac,f1))).)

3. Generate the conjugate transpose of U(_(ac,f1)), U*_((ac,f1)). (Notethat since U_((ac,f1)) is unitary, the conjugate transpose ofU_((ac,f1)) is the same as the inverse and the adjoint of U_((ac,f1)).)

4. Generate ΔV_((n,ac,meas)) by subtracting the voltages expected at theaccessible nodes if all the components are at their nominal value fromthe voltages measured at the accessible nodes.

5. Multiply U*_((ac,f1)) and ΔV_((n,ac,meas)).

6. If the first r elements (rows) of U*_((ac,f1))ΔV_((n,ac,meas)) arenon-zero, and the rest are zero, then the equivalence class ℑ of whichf1 is a member, contains the group of components that are non-nominal.The variable r represents the maximum number of simultaneouslynon-nominal (or faulty) components that is being checked. (r also equalsrank(Z_((ac,f1)).) If the group of components f1 is only one member ofℑ, then all of the components in f1 are non-nominal.

7. If the first r elements (rows) of U*_((ac,f1))ΔV_((n,ac,meas)) arenot all non-zero, or any of the rest of the elements are non-zero, thenthe equivalence class ℑ of which f1 is a member, does not contain thegroup of components that are non-nominal. Accordingly, steps 1-6 shouldbe repeated for each equivalence class, in turn, until one is found thatcontains the non-nominal group of components or all the equivalenceclasses are exhausted.

In a preferred embodiment, steps 1-3, above, are typically performedjust once for each equivalence class and the resulting U*_((ac,f1)),U*_((ac,f2)) . . . etc. matrices are typically stored for use in step 5.Step 4 is usually performed once for each circuit tested. Additionally,steps 5 and 6 are typically repeated successively for each equivalenceclass until the an equivalence class (if any) is found that contains thegroup of components that are non-nominal on the circuit being tested. Noequivalence class may be found if there are more simultaneouslynon-nominal components than were considered when the equivalence classeswere constructed.

On many circuits, components are not confined to a single nominal valueto be considered non-faulty. This range of component values is referredto as tolerance. In a preferred embodiment, to cope with componenttolerances, the zero/non-zero determination in step 6, above, isgoverned by the following equation:

γ_((ac,f,min)) ≦U* _((ac,f)) ΔV _((n,ac,meas))≦γ_((ac,f,max))   (EQN.19)

In other words, if a particular element of U*_((ac,f))ΔV_((ac,test)) isgreater than or equal to the γ_((ac,f,min)) (for that particular elementand that particular fault “f”) and U*_((ac,f))ΔV_((ac)) is less than orequal to the γ_((ac,f,max)) (for that particular element and thatparticular fault “f”), then it should be considered a zero. Otherwise,it should be considered non-zero. “Considering” a particular element ofU*_((ac,f))ΔV_((n,ac,meas)) zero/non-zero allows the tests of steps 6and 7, above, to be accomplished in the case where components havetolerances. However, by allowing a range of values for each element ofU*_((ac,f))ΔV_((n,ac,meas)) to be considered zero/non-zero, more thanone equivalence class may satisfy the test of step 6 and be identifiedas containing a member whose components are out-of-tolerance.Nevertheless, the search for the out-of-tolerance components should beconsiderably narrowed since only one equivalence class truly has amember whose components are out-of-tolerance.

If U*ΔV_((n,ac,meas)) involves complex numbers, the real and imaginaryparts must both satisfy limits to be considered a zero. Thezero/non-zero determination is governed by the following set ofequations:

γ_((ac,f,real,min))≦real(U* _((ac,f)) ΔV_((n,ac,meas))≦γ_((ac,f,real,max))

γ_((ac,f,imag,min))≦imag(U* _((ac,f)) ΔV_((n,ac,meas))≦γ_((ac,f,imag,max))   (EQN. 20)

In both Equations 19 and 20, the γ terms are vectors with individualelements that each correspond to a row of U*. The functions real( ) andimag( ) extract the real and imaginary parts, respectively, from theinput vector.

In an exemplary embodiment, any method that will minimize and maximize afunction is sufficient to calculate each element of the γ vectors. In apreferred embodiment, the method used to calculate the γ vectors isbased on linear programming (LP). One matrix formulation of an LP modelis called a standard-form LP and is characterized by:

min(or max) cx

subject to Dx=b, x≧0.

To construct the constraint matrix, D, the tolerance on the elements ofK_(v) can be viewed as a tolerance on the real portion of the complexvalues and a tolerance on the imaginary portion. If only one portionexists (e.g. in the case of a resistor, the corresponding value in K_(v)is a real number, and for a capacitor, the value has only an imaginaryportion), the tolerance on the other portion is zero. Since linearprogramming has the implicit constraint that all decision variables arerequired to be greater than or equal to zero, the tolerance on the realportions of K_(v) can be described by:

real(min K _(v))≦real(K _(v))≦real(max K _(v))

real(K _(v))=X _(real)+real(min K _(v))=real(max K _(v))−Y _(real) withX _(real)≦0 and Y _(real)≧0

X _(real) +Y _(real)=real(max K _(v))−real(min K _(v)).

Similarly, the imaginary portions of K_(v) can be described by:

X _(imag) +Y _(imag)=imag(max K_(v))−imag(min K_(v)).

The above equations may then be written as follows: $\begin{matrix}{{\begin{bmatrix}\begin{bmatrix}1 & 0 & \cdots & \cdots & 0 & 1 & 0 & \cdots & 0 & 0 \\0 & 1 & 0 & \cdots & 0 & 0 & 1 & 0 & \cdots & 0 \\\vdots & ⋰ & \vdots & \vdots & \vdots & \vdots & ⋰ & \vdots & \vdots & \vdots \\0 & \cdots & 0 & 1 & 0 & 0 & \cdots & 0 & 1 & 0 \\0 & \cdots & 0 & 0 & 1 & 0 & \cdots & 0 & 0 & 1\end{bmatrix} & 0 \\0 & \begin{bmatrix}1 & 0 & \cdots & \cdots & 0 & 1 & 0 & \cdots & 0 & 0 \\0 & 1 & 0 & \cdots & 0 & 0 & 1 & 0 & \cdots & 0 \\\vdots & ⋰ & \vdots & \vdots & \vdots & \vdots & ⋰ & \vdots & \vdots & \vdots \\0 & \cdots & 0 & 1 & 0 & 0 & \cdots & 0 & 1 & 0 \\0 & \cdots & 0 & 0 & 1 & 0 & \cdots & 0 & 0 & 1\end{bmatrix}\end{bmatrix}\begin{bmatrix}\begin{bmatrix}X_{{real},1} \\\vdots \\X_{{real},b} \\Y_{{real},1} \\\vdots \\Y_{{real},b}\end{bmatrix} \\\begin{bmatrix}X_{{imag},1} \\\vdots \\X_{{imag},b} \\Y_{{imag},1} \\\vdots \\Y_{{imag},b}\end{bmatrix}\end{bmatrix}} = \begin{bmatrix}\begin{bmatrix}{{{real}( {\max \quad K_{v,11}} )} - {{real}( {\min \quad K_{v,11}} )}} \\{{{real}( {\max \quad K_{v,22}} )} - {{real}( {\min \quad K_{v,22}} )}} \\\vdots \\{{{real}( {\max \quad K_{v,{bb}}} )} - {{real}( {\min \quad K_{v,{bb}}} )}}\end{bmatrix} \\\begin{bmatrix}{{{imag}( {\max \quad K_{v,11}} )} - {{imag}( {\min \quad K_{v,11}} )}} \\{{{imag}( {\max \quad K_{v,22}} )} - {{imag}( {\min \quad K_{v,22}} )}} \\\vdots \\{{{imag}( {\max \quad K_{v,{bb}}} )} - {{imag}( {\min \quad K_{v,{bb}}} )}}\end{bmatrix}\end{bmatrix}} & ( {{EQN}.\quad 21} )\end{matrix}$

Note that the above equation 21 has the form Dx=b which matches the formof the constraints in a standard-form LP.

The objective function to be optimized corresponds to each row of theright-hand side of the following equation derived from Equation 17:

U* _((ac,f)) ΔV(_(n,ac)) =U* _((ac,f)) Z _((ac)) ΔK _(v) A ^(T)(V _(n)+ΔV _(n))=[U* _((ac,f)) ][Z _((ac))][diag(A ^(T)(V _(n) +ΔV_(n)))][vect(ΔK _(v))]  (EQN. 22)

Here the diag( ) function transforms a vector into a diagonal matrixwhere the diagonal elements are the vector elements and the rest of theelements are zero. The vect( ) function takes the elements along thediagonal of a diagonal matrix and transforms them into a vector. Thishas the form cx where ΔK_(v) is the decision variable. Written in termsof X₁ (the real part) and X₂ (the imaginary part) of ΔK_(v), and E₁ (thereal part) and E₂ (the imaginary part) of the rest of Equation 22results in: $\begin{matrix}{{{{\lbrack U_{({{a\quad c},f})}^{*} \rbrack \lbrack Z_{({a\quad c})} \rbrack}\lbrack {{diag}( {A^{T}( {V_{n} + {\Delta \quad V_{n}}} )} )} \rbrack}\begin{bmatrix}{X_{{real},1} + {j\quad X_{{imag},1}}} \\{X_{{real},2} + {j\quad X_{{imag},2}}} \\{X_{{real},3} + {j\quad X_{{imag},3}}} \\\vdots \\{X_{{real},b} + {j\quad X_{{imag},b}}}\end{bmatrix}} = {{( {E_{real} + {j\quad E_{imag}}} )( {X_{real} + {j\quad X_{imag}}} )} = {( {{E_{real}X_{real}} - {E_{imag}X_{imag}}} ) + {j( {{E_{real}X_{imag}} + {E_{imag}X_{real}}} )}}}} & ( {{EQN}.\quad 23} )\end{matrix}$

By separating Equation 23 into its real and imaginary components, twoobjective functions are obtained. One objective function is for the realpart of Equation 23, and one is for the imaginary part. Each of theseobjective functions needs to be minimized and maximized independently.The objective functions are: $\begin{matrix}{H_{real} = \begin{matrix}\lbrack E_{real}  & 0 & {- E_{imag}} & { 0 \rbrack \begin{bmatrix}\begin{bmatrix}X_{{real},1} \\X_{{real},2} \\\vdots \\X_{{real},b} \\Y_{{real},1} \\Y_{{real},2} \\\vdots \\Y_{{real},b}\end{bmatrix} \\\begin{bmatrix}X_{{imag},1} \\X_{{imag},2} \\\vdots \\X_{{imag},b} \\Y_{{imag},1} \\Y_{{imag},2} \\\vdots \\Y_{{imag},b}\end{bmatrix}\end{bmatrix}}\end{matrix}} & ( {{EQN}.\quad 24} ) \\{{and}\quad} & \quad \\{H_{imag} = {\begin{matrix}\lbrack E_{imag}  & 0 & E_{real} & { 0 \rbrack \begin{bmatrix}\begin{bmatrix}X_{{real},1} \\X_{{real},2} \\\vdots \\X_{{real},b} \\Y_{{real},1} \\Y_{{real},2} \\\vdots \\Y_{{real},b}\end{bmatrix} \\\begin{bmatrix}X_{{imag},1} \\X_{{imag},2} \\\vdots \\X_{{imag},b} \\Y_{{imag},1} \\Y_{{imag},2} \\\vdots \\Y_{{imag},b}\end{bmatrix}\end{bmatrix}}\end{matrix}.}} & ( {{EQN}.\quad 25} )\end{matrix}$

These functions are non-linear because both ΔK_(v) and ΔV_(n) areunknown. This can be solved by appropriately assigning values for theelements in ΔV_(n) and then optimizing to find a ΔK_(v). FIG. 6 is aflow chart illustrating the steps to optimize the objective functionconsistent with an embodiment of the present invention. In a step 602,the process is initiated by setting ΔV_(n) to zero. In a step 604, theobjective function is optimized using the currently assigned value ofΔV_(n) and the conventional Simplex method. This optimization may be aminimization or a maximization depending on whether γ_((ac,f,min)) orγ_((ac,f,max)), respectively, is being computed. In a step 606, if thisis the first pass (i.e. ΔV_(n) is zero) then the process proceeds tostep 608. If it is not the first pass, then the process continues on tostep 610. In a step 608, ΔV_(n) is calculated from the ΔK_(v) found instep 604. The process then loops back to step 604 to optimize theobjective function with the new ΔV_(n).

In a step 610, if the objective function optimized in step 604 improvedfrom the last pass, the process continues on to step 614. If theobjective function did not improve from the last pass, k is divided bytwo in step 612 and then the process continues on to step 614. In a step614 a new ΔV_(n) is calculated as ΔV_(n)←(1+k)ΔV_(n). In a step 616, acheck is made to see how close the new ΔV_(n) is to the last ΔV_(n). Ifthe new ΔV_(n) is close enough, for example 1%, then the processterminates in a step 618. If the new ΔV_(n) is not close enough, thenthe process loops back to step 604 to optimize the objective functionagain with the new ΔV_(n).

The steps in FIG. 6 constitute a search method that finds a ΔV_(n) thatproduces the most optimized objective function. In this case, it is abisection search. Other search methods could also be used. Examples oftypes of searches that could be used are the Fibonacci search, theGolden Section search, and the Dichotomus search. Other types ofsearches that could be used are detailed in Algorithms by R. Sedgewick,Addison-Wesley, Reading Mass., 1988.

Once the algorithm terminates with a ΔK_(v) that either minimizes ormaximizes the objective function, the real and imaginary γ_((ac,f,min))and γ_((ac,f,max)) may be obtained from the optimization routine, or maybe computed from ΔK_(v) and the last ΔV_(n) using the following equationderived from the objective function:

γ_((ac,f)) =U* _((ac,f)) ΔV _((n,ac)) =U* _((ac,f)) Z _((ac)) ΔK _(v) A^(T)(V _(n) +ΔV _(n))   (EQN. 26)

Referring back to FIG. 2, in a step 210 stimulus and measurement nodesare selected to test each component in the reduced cluster. This processof selecting stimulus and measurement nodes could be performed on largergroups of components including clusters or the whole circuit. However,in a preferred embodiment the process of selecting uses the componentsin a reduced cluster to improve efficiency and reduce complexity. Toillustrate the process of selecting stimulus and measurement nodes takeFIG. 7 to be an example reduced cluster. FIG. 7 shows the circuit ofFIG. 4 with the current source as provided by the test hardware, J₁,removed consistent with an embodiment of the present invention. In theexample illustrated in FIG. 7, nodes V_(n0), V_(n1) and V_(n2) are onceagain the only accessible nodes. FIG. 4 illustrates just one of threepossible stimulus locations that could have been chosen. The other twoare illustrated in FIG. 8 and FIG. 9 consistent with an embodiment ofthe present invention. Note how in FIG. 4 and FIG. 8 current will flowthrough R₁, but in FIG. 9 no current will flow through R₁. Clearly, thestimulus location shown in FIG. 9 should not be selected to try to testR₁.

To test a component, it is desirable to maximize the voltage across thatcomponent. This maximizes the dependence of at least one node voltage onthe component value. A process for choosing stimulus that maximizes thebranch voltage across a component is illustrated in FIG. 10 consistentwith an embodiment of the present invention.

In a step 1022, the branch voltages for each component are calculatedfor each stimulus location. In a step 1024, a cost function is appliedto the branch voltages. A cost function might be used to reduce (orincrease) the desirability of some stimulus over others. For example,voltage measurements at lower frequencies take longer because themeasurement interval needs to include the period (or fraction thereof)of the applied frequency. To accommodate a cost function, the nodevoltage values of various stimulus may be multiplied by appropriatenumbers. Multiplication by a positive number less than one lowers thedesirability of that stimulus. Multiplication by positive numbersgreater than 1 increases the desirability of that source.

In a step 1026, the magnitude of the branch voltage values are sortedand duplicate values are eliminated. In a preferred embodiment, they aresorted from smallest to largest. In a step 1028, non-useful branchvoltages are set to zero. Also in step 1028, branch voltages that arewithin a predetermined factor of each other are rounded to the samevalue. A branch voltage across a device is only considered useful if achange in that voltage is detectable between when the device has itsminimum value and when the device has its maximum value. For example, achange in voltage may only be detectable if it is greater than 1microvolt. Branch voltages that may result in changes between a devicesminimum and maximum value being undetectable may be assigned a zero rankorder number, or equivalently, set to zero before rank ordering. Thishelps take into account the tester hardware's measuring accuracy bytreating branch voltages that may not result in detectable changes as“no-current” branches.

Finally, branch voltages that are within a predetermined factor of eachother may be rounded to the same number or assigned the same rank order.In a preferred embodiment, branch voltages that are within a factor oftwo are rounded to the same number before rank ordering. This roundinghelps reduce the number of stimuli selected.

In a step 1030, each branch voltage value is then assigned a rank ordernumber. In a preferred embodiment, the rank order number is assignedstarting at zero with the smallest branch voltage and incrementing byone for each different branch voltage value. However, this choice isarbitrary. Many other ranking schemes such as a descending rank order,or incrementing by a different positive or negative number could be usedas long as appropriate adjustments were made in later steps whendetermining which stimulus to use.

In a step 1032, if any of the branch voltages was zero, then one issubtracted from all the rank order numbers. In a step 1034, a figure ofmerit is assigned to each stimulus location. The figure of merit istypically assigned by replacing the branch voltage value across eachcomponent with the corresponding rank order number and then summing therank order numbers for every component.

In a step 1036, the stimulus location or locations are selected. Thestimulus locations may be chosen according to a variety of criteriabased on the information developed in the preceding steps. For example,if maximum branch voltages are desired, the following process may beused:

1. Eliminate any stimulus locations that do not have the highest rankorder number for at least one component.

2. Select the stimulus location with the highest figure of merit.

3. Eliminate from consideration those components that the selectedstimulus location has the highest rank order number for that component.

4. Eliminate the selected stimulus from consideration.

5. Repeat steps 1-4 until there are no more components underconsideration.

A way of selecting the stimulus locations to maximize test throughputmay be to first select the stimulus (if any) with the highest figure ofmerit that did not have any components with a zero rank order number. Ifthere was no stimulus location that did not have at least one componentwith a zero rank order number, the stimulus with the highest figure ofmerit would be selected and all the components with non-zero rank ordernumbers eliminated from consideration. Then another stimulus locationcould be selected and the process repeated until there were no morecomponents under consideration.

A third way of selecting stimulus locations would be to balancemaximizing branch voltages with test throughput. This may beaccomplished by adding one stimulus at a time until a desired measure ofcoverage and/or measure of time is achieved. This balance can be left tothe user, or selected through an automated set of rules.

An example of the processes in FIG. 10 follows. For simplicity, thestimulus location shown in FIG. 4 will be referred to as σ_(n1,n0) sincethe stimulus is between nodes V_(n1) and V_(n2). Likewise, the stimuluslocation shown in FIG. 8 will be referred to as σ_(n2,n1) and thestimulus location shown in FIG. 9 will be referred to as σ_(n2,n0). Whenthe branch voltages for each of these three stimulus are presented intable form, Table 2 results.

TABLE 2 R₁ R₂ R₃ R₄ R₅ σ_(n1,n0) 1 1 0.5 0.25 0.25 σ_(n2,n0) 0 0.5 0.250.375 0.375 σ_(n2,n1) 1 0.5 0.75 0.125 0.125

Examining Table 2, it can be observed that stimulus location σ_(n2,n1)creates a larger voltage across R₃ than either of the other two stimuluslocations. Likewise, it can be seen that stimulus locations σ_(n1,n0)and σ_(n2,n1) produce equal voltages across R₁ and σ_(n2,n0) produces novoltage across R₁.

When the branch voltage in Table 2 are sorted according to their values,duplicates eliminated, and a rank order number assigned, Table 3results.

TABLE 3 Branch Voltage Rank Order 0 0 0.125 1 0.25 2 0.375 3 0.5 4 0.755 1 6

When the branch voltage values in Table 2 are replaced by thecorresponding rank order number and a figure of merit calculated, Table4 results.

TABLE 4 R₁ R₂ R₃ R₄ R₅ Figure of Merit σ_(n1,n0) 6*  6* 4 2 2 20σ_(n2,n0) 0  4 2  3*  3* 12 σ_(n2,n1) 6* 4  5* 1 1 17

Note that in Table 4, the best (i.e. highest rank order) entries foreach component have been marked with an asterisk (*). The information inTable 4 can then be used to apply the appropriate stimulus selectioncriteria.

The preceding example was based upon a purely resistive circuit and thestimulus was limited to a single frequency (including DC). Whencomponents other than resistors are part of the circuit, complex nodevoltages may be present. These node voltages are a function of stimulusfrequency. The method previously described may be extended to cover thissituation by calculating the branch voltages with the stimulus at anumber of frequencies at each possible source location. The normalprocess of rank ordering, calculating a figure of merit, and selectioncould then be used to select which stimulus location or locations andwhich frequencies are to be used. The form of Table 2 when extended tomultiple frequencies is shown by Table 5.

TABLE 5 Component Component Component Last #1 #2 #3 ... Componentσ_(n1,n0,DC) σ_(n1,n0,freq1) σ_(n1,n0,freq2) ... σ_(n2,n0,DC)σ_(n2,n0,freq1) ... σ_(n2,n1,DC) σ_(n2,n1,freq1) ...

As stated previously, a cost function may also be applied to the branchvoltage data. A cost function might be used to reduce (or increase) thedesirability of some stimulus over others. For example, voltagemeasurements at lower frequencies take longer because the measurementinterval needs to include the period (or fraction thereof) of theapplied frequency. To accommodate a cost function, the node voltagevalues of various stimulus may be multiplied by appropriate numbers.Multiplication by a positive number less than one lowers thedesirability of that stimulus. Multiplication by positive numbersgreater than 1 increases the desirability of that source. The costfunction used in the above example was one.

As discussed above in step 1028, the branch voltage values that may notresult in detectable changes are set to zero, or assigned a zero rankorder number. A rule of thumb process for determining which branchvoltage values to set to zero is based on Equation 27. $\begin{matrix}{\frac{2t\quad V_{b}}{100} > {threshold}} & ( {{EQN}.\quad 27} )\end{matrix}$

where V_(b) is the branch voltage value being checked and t is thetolerance, in percent, of the device that is the branch being checked.Threshold is a predefined number that reflects the accuracy of themeasurement hardware. In a preferred embodiment, if the tester hardwarecannot detect voltage differences less than 1 microvolt, then thresholdwould be chosen to be 1 microvolt.

In a preferred embodiment, an efficient method of calculating Table 2 isused. A model of the cluster in the Simplified Tableau form of Equation1 is the starting point. Equation 1 is given again for convenience.$\begin{matrix}{{\begin{bmatrix}K_{i} & {{- K_{v}}A^{T}} \\A & 0\end{bmatrix}\begin{bmatrix}I_{b} \\V_{n}\end{bmatrix}} = \begin{bmatrix}S \\0\end{bmatrix}} & ( {{EQN}.\quad 1} )\end{matrix}$

Note that by arranging the rows of Equation 1 to separate the branchcurrents of the components under test and the branch currents of thecurrent source branches, (assuming there are no voltage sources) I_(b)can be written as: $\begin{matrix}{I_{b} = \begin{bmatrix}I_{({b,{test}})} \\{- J}\end{bmatrix}} & ( {{EQN}.\quad 28} )\end{matrix}$

where I_((b,test)) is the set branch currents of the components undertest in vector form and −J is the set of branch currents in the currentsource branches (in vector form.) Making similar splits in K_(i), K_(v),and A gives: $\begin{matrix}{K_{i} = \begin{bmatrix}1 & 0 \\0 & 1\end{bmatrix}} & ( {{EQN}.\quad 29} ) \\{K_{v} = \begin{bmatrix}K_{({v,{test}})} & 0 \\0 & 0\end{bmatrix}} & ( {{EQN}.\quad 30} ) \\{A = \begin{bmatrix}A_{({b,{test}})} & A_{J}\end{bmatrix}} & ( {{EQN}.\quad 31} )\end{matrix}$

where 1 is an identity matrix so K_(i) equals an identity matrix.K_((v,test)) is the submatrix of K_(v) that corresponds to thecomponents under test. The entries in K_(v) corresponding to the currentsources are zero. Substituting Equations 29-31 into Equation 1 andsimplifying gives: $\begin{matrix}{{\begin{bmatrix}1 & {{- K_{({v,{test}})}}{A^{T}}_{({b,{test}})}} \\A_{({b,{test}})} & 0\end{bmatrix}\begin{bmatrix}I_{({b,{test}})} \\V_{n}\end{bmatrix}} = \begin{bmatrix}0 \\{A_{J}J}\end{bmatrix}} & ( {{EQN}.\quad 32} )\end{matrix}$

Remember that A_((b,test)), I_((b,test)), and K_((v,test)) normallycorrespond to the components under test and do not correspond to anyportion of the stimulus. Branch voltages are related to node voltagesby:

V _(b) =A ^(T) V _(n)   (EQN. 33)

Equation 33 can be separated into the branch voltages of the componentsunder test and the branch voltages of the current source branchessimilar to the way I_(b) was separated in Equation 2 to give:$\begin{matrix}{V_{b} = {\begin{bmatrix}v_{b} \\v_{J}\end{bmatrix} = {{A^{T}V_{n}} = {\begin{bmatrix}A_{({b,{test}})}^{T} \\A_{J}^{T}\end{bmatrix}V_{n}}}}} & ( {{EQN}.\quad 34} )\end{matrix}$

Solving Equation 32 for $\begin{bmatrix}I_{({b,{test}})} \\V_{n}\end{bmatrix}$

and partitioning gives: $\begin{matrix}{\begin{bmatrix}I_{({b,{test}})} \\V_{n}\end{bmatrix} = {{\begin{bmatrix}1 & {{- K_{({v,{test}})}}{A^{T}}_{({b,{test}})}} \\A_{({b,{test}})} & 0\end{bmatrix}^{- 1}\begin{bmatrix}0 \\{A_{J}J}\end{bmatrix}} = {\begin{bmatrix}\quad & \quad \\\quad & W\end{bmatrix}\begin{bmatrix}0 \\{A_{J}J}\end{bmatrix}}}} & ( {{EQN}.\quad 35} )\end{matrix}$

It follows from Equation 34 and Equation 35 that:

v_(b) =A ^(T) _((b,test)) V _(n) =A ^(T) _((b,test)) WA _(J) J   (EQN.36)

In a preferred embodiment, the stimulus is applied one source at a timeso the vector J has a single non-zero entry associated with the branchcurrent and zero elsewhere. Applying this stimulus results in a vectorof branch voltage, v_(b). If J was defined as a matrix of singlestimulus locations where the stimulus is only at possible stimuluslocations as shown in Equation 37, there is now a single current sourceat each possible stimulus location. $\begin{matrix}{J_{matrix} = \begin{bmatrix}J_{1} & 0 & 0 & \cdots & 0 \\0 & 0 & 0 & \quad & J_{{n{({n - 1})}}/2} \\\vdots & \vdots & \vdots & \quad & 0 \\0 & 0 & J_{3} & \cdots & 0 \\0 & J_{2} & 0 & \cdots & 0\end{bmatrix}} & ( {{EQN}.\quad 37} )\end{matrix}$

When v_(b) is calculated using the J_(matrix) of Equation 37, the resultis a matrix where the rows are branch voltages and the columns are thepossible source configurations. For convenience, J_(matrix) could alsobe arranged so that it was diagonal. If this is transposed, a matrixwith entries in the form of Table 2 results:

table=(A ^(T) _((b,test)) WA _(J) J _(matrix))^(T)   (EQN. 38)

Equation 38 can be extended to support multiple frequencies. Theoriginal system of equations can be used to analyze multiple frequenciesby repeating the calculations with the stimulus at differentfrequencies.

The preceding discussions have focused mainly on the case where theapplied stimulus and measurement hardware are ideal or have negligiblenon-ideal properties. However, due to practical considerations involvingthe design and construction of modern test hardware, both the hardwarefor applying stimulus, and the measurement apparatus may havesignificant non-ideal properties. These non-ideal properties are alsocalled parasitic effects. This situation is illustrated in FIGS. 11A and11B consistent with an embodiment of the present invention. FIGS. 11Aand 11B show the same situation except in FIG. 11A the ideal stimulus isshown as a voltage source 1104, V_(s), in series with a source impedanceresistor 1106, R_(s). In FIG. 11B, the ideal stimulus is shown as thethevenin equivalent of the stimulus in FIG. 11A—a current source 1118 inparallel with source impedance resistor 1120, also R_(s).

In both FIGS. 11A and 11B, the set of components being tested are showngenerally as shape 1102. The non-ideal properties of the stimulushardware are shown as resistances 1114, Z_(i), 1116, Z_(g), andcapacitance 1108, y_(s). The non-ideal properties of the measurementhardware are shown as capacitance 1112, y_(d), connected to a node fromthe set of components being tested, and the reference node. Since thedominant component of the non-ideal properties of the detector and thesource tends to be capacitance, y_(s) and y_(d) are shown as beingcapacitive. However, these could also include resistive and inductiveproperties. Z_(i) and Z_(g) represent various resistance associated withapplying the stimulus to the circuit being tested.

These non-ideal properties of the stimulus hardware, like the stimulusitself, can be included in the model of the set of components. However,the values for Z_(i) and Z_(g) may change each time a board is testedsince the resistance between a board and the probe pin, among otherthings, may change each time a board is placed in the test fixture.Finally, in a case where there are fewer detectors than nodes that needto be measured, the non-ideal properties of the measurement hardwarewill move from node to node with each measurement location. This meansthe value of y_(d) will change from measurement location to measurementlocation. The values of y_(d) and y_(s) will also change each time atest fixture is mounted. Accordingly, instead of including all thesenon-ideal properties in the model when running the test programgenerator, in a preferred embodiment, a test-time correction is runwithin the fault analysis routine to translate the values measured bythe measurement hardware to values that would have been measured if themeasurement and stimulus hardware were ideal. These corrected values maythen be used with the U* matrices and test limits generated from a modelwithout these non-ideal properties.

FIG. 15 illustrates a process that corrects for these non-idealproperties to produce a corrected version of the measured voltages ataccessible nodes that approximates the voltages that would be on themeasured accessible nodes had the measurement and stimulus hardware beenideal consistent with an embodiment of the present invention. Before themeasured voltages are corrected, several measurements are typicallytaken to characterize the test hardware. One of these measurements istaken of the source voltage, V_(s). This is an AC voltage so that latermeasurements taken with this voltage applied can be used to calculatethe capacitance on certain nodes. In particular, this measured value isused to calculate the testhead capacitance for both the source and thedetector.

Several other hardware dependant measurements may also be taken. Thesemeasurements allow calculation of the parasitic capacitance on eachsource node and the reference node due to the tester hardware. Thisprovides the y_(s) value for each applied stimulus location. Likewise,these measurements also allow the calculation of the parasiticcapacitance on each measurement node due to the tester hardware. Thisprovides the y_(d) value for each node measured by the measurementhardware. Since the parasitic capacitance of the detector will vary frommeasurement node to measurement node, the notation y_((X,d)) is used toindicate the value of the parasitic capacitance at node X (in admittanceform) due to the detector being connected to that node. The sourcecapacitance will vary from stimulus location to stimulus location.However, the following discussion is directed to only one sourcestimulus location so only the notation y_(s) is used to indicate thesource capacitance (in admittance form). This process may be expanded tomore stimulus location by using different values of y_(s) and differentincidence matrixes for each stimulus location.

After these characterization measurements have been taken, stimulus maybe applied to, and measurements taken on, a board. These measurementscan then be corrected for the non-ideal properties of the testenvironment according to FIG. 15.

In a step 1502, the voltage measurements are taken. These measurementsare taken with stimulus applied to the board. The results of thesemeasurements are stored in a vector, V′_(m). To indicate an individualelement of this vector, say the node voltage at node X the notationV′_((X,m)) will be used in this discussion. Similar notation will beused to indicate individual elements of other vector quantities.

In addition to measuring the node voltages on the accessible nodes onthe board (or alternatively, the cluster, or reduced cluster) undertest, measurements are taken of the reference node on the board, V′_(g),the applied stimulus voltage to the board, V′_(s) and the voltage on theopposite side of the reference resistor from the stimulus source,V′_(r). In a step 1504, the measurement taken on the opposite side ofthe reference resistor, V′_(r) is corrected using Equation 39 for theeffects of the detector capacitance as it measured the voltage V′_(r).The detector capacitance as it measured the voltage V′_(r) is y_((r,d)).$\begin{matrix}{V_{r}^{\prime\prime} = \frac{V_{s}^{\prime} \cdot V_{r}^{\prime}}{V_{s}^{\prime} - ( {y_{({r,d})} \cdot R_{s}^{\prime} \cdot V_{r}^{\prime}} )}} & ( {{EQN}.\quad 39} )\end{matrix}$

In a step 1506, a Z matrix is calculated using the nominal values of theboard components, default values for Z_(i) and Z_(g), and a measuredvalue of the reference resistor, R′_(s). Default values for Z_(i) andZ_(g) may be developed by measuring and averaging Z_(i) and Z_(g) for anumber of different paths from a source to a board under test. Thetableau should also contain entries for y_(d) and y_(s). The entry fory_(s) may be a column in the incidence matrix that defines a connectionbetween V_(r) and the reference node and an entry in the K_(v) matrix ofzero. The entry for y_(d) may be a column in the incidence matrix thatis all zeros and an entry in the K_(v) matrix of zero. This allows thevalue y_((X,d)) to be plugged into a later equation to calculate thecorrected node voltage on node X without having to re-calculate a Zmatrix or repeat several other steps shown in FIG. 15. In a step 1508,the measured voltage on the reference node on the board, V′_(g), and themeasured voltage at the stimulus node on the board, V′_(i), arecorrected to remove the effects of the detector capacitance, y_(d), andthe source capacitance, y_(s). V′_(g) is corrected using Equation 40.V′_(i) is corrected using Equation 41.

V″ _(g) =V′ _(g)·(1−Z _((g,d)) ·y _((g,d)))−(Z _((g,s)) ·y _(s) ·V″_(r))   (EQN. 40)

V″ _(i) =V′ _(i)·(1−Z _((i,d)) ·y _((i,d)))=(Z _((i,s)) ·y _(s) ·V″_(r))   (EQN. 41)

In Equations 40 and 41, Z_((g,d)) is the entry in the last calculated Zmatrix corresponding the node V_(g) and the column corresponding to thebranch y_(d). Similarly, Z_((i,d)) is the entry in the last calculated Zmatrix corresponding the node V_(i) and the column corresponding to thebranch y_(d). Z_((g,s)) is the entry in the last calculated Z matrixcorresponding the node V_(g) and the column corresponding to the branchy_(s). Z_((i,s)) is the entry in the last calculated Z matrixcorresponding the node V_(i) and the column corresponding to the branchy_(s).

In a step 1510, V″_(r) is corrected for the effects of the sourcecapacitance, y_(s) using Equation 42. $\begin{matrix}{V_{r}^{*} = \frac{V_{s}^{\prime} \cdot V_{r}^{\prime\prime}}{V_{s}^{\prime} - ( {y_{s} \cdot R_{s}^{\prime} \cdot V_{r}^{\prime\prime}} )}} & ( {{EQN}.\quad 42} )\end{matrix}$

In a step 1512, Z_(i) and Z_(g) are calculated using Equations 43 and44, respectively. $\begin{matrix}{Z_{i} = \frac{R_{s}^{\prime}( {V_{r}^{*} - V_{i}^{\prime\prime}} )}{V_{s}^{\prime} - V_{r}^{*}}} & ( {{EQN}.\quad 43} ) \\{Z_{g} = \frac{R_{s}^{\prime}V_{g}^{\prime\prime}}{V_{s}^{\prime} - V_{r}^{*}}} & ( {{EQN}.\quad 44} )\end{matrix}$

In a step 1514, if this was a first calculation of Z_(i) and Z_(g), thenthe process needs to iterate one more time and proceeds to step 1516. Ifthis is the second time Z_(i) and Z_(g) have been calculated, then theprocess proceeds to step 1518.

In a step 1516, a Z matrix is calculated using values for Z_(i) andZ_(g) just calculated. These values for Z_(i) and Z_(g) should beplugged into the tableau as admittances in the K_(v) matrix. The processthen loops back to step 1508 where the just calculated Z matrix willsupply some of the terms in Equations 42 and 43.

In a step 1518, yet another Z matrix is calculated. This matrix iscalculated using the Z_(i) and Z_(g) from the second iteration of steps1508 through 1512. The measured value of the reference resistor R′_(s)is also used in the calculation of the Z matrix. This matrix is thenreduced to include only those rows that correspond to nodes that weremeasured in step 1502. This reduced matrix is Z_(corr). In a step 1520,a first correction is calculated on each measured node voltage. IfV′_((x,m)) is the measured voltage at node X, then the first correctionis calculated according to Equation 45.

V″ _((x,m)) =V′ _((x,m)) ·Z _((x,d)) ·y _((x,d)) −V* _(r) ·Z _((x,s)) ·y_(s)   (EQN. 45)

In a step 1522, a measured value of the current flowing through thereference resistor, I′_(s), is calculated using Ohm's law from themeasure value of the source, V′_(s), and the measured value of thereference resistor, R′_(s). I′_(s) is then placed in a source vector S′.The source vector S′ is created by placing I∝0 _(s) in a vector with onecolumn and a row corresponding to each branch in the circuit. I′_(s) isplaced in the row corresponding to the stimulus branch. The rest of therow entries are zero.

In a step 1524, a modified model voltage is calculated for each measurednode using Equation 46. V_(mod) is a vector with one column and each rowcorresponding to a measured node.

V _(mod) =Z _(corr) S′  (EQN. 46)

In a step 1526, a correction factor C_(f) is calculated for eachmeasured node. This correction factor is simply the ideal value of eachmeasured node voltage as modeled by a tableau with nominal componentsand no detector or source parasitic effects divided by the modifiedmodel voltage for that node. This is shown in Equation 47.$\begin{matrix}{C_{x} = \frac{V_{({x,{ideal}})}}{V_{({x,{mod}})}}} & ( {{EQN}.\quad 47} )\end{matrix}$

Finally, in a step 1528, a corrected value for each measured nodevoltage is generated using Equation 48.

V _((x,corrected)) =C _(x) V″ _((x,m))   (EQN. 48)

These values can then be used to create the vector ΔV_((n,ac,meas))which can be multiplied by the various U* matrices and checked againstlimits that were generated using a tableau model that did not includethe non-ideal properties of the tester hardware.

Many measurement systems will have some error in the measurements theytake. This error means that a reported measurement of say 0.15 volts,for example, could correspond to an actual node voltage of as little as0.149 volts or as much as 0.151 volts. Accordingly, each individualmeasured node voltage in V′_(m) could be inside a range with a minimumand a maximum. For example, node X could be within the range given byEquation 49.

V′ _((X,m,min)) ≦V′ _((X,m,actual)) ≦V′ _((X,m,max))   (EQN. 49)

These minimums and maximums can be calculated from the V′_((X,m)) thatis returned by the measurement hardware. They may then be passed throughthe procedure detailed in FIG. 15, above, to produce a V_(corr,min) anda V_(corr,max). V_(corr,min) and V_(corr,max) may then be used toproduce a ΔV_(min) and a ΔV_(max). These ranges for the individualelements of ΔV may be used as the constraints in a linear programmingproblem to find a minimum U*ΔV and a maximum U*ΔV. Equations 50 and 51may then be used to govern whether a particular entry in U*ΔV should beconsidered zero or non-zero.

γ_((ac,f,min)) ≦LP _(min)(U* _((ac,f)) ΔV)≦γ_((ac,f,max))   (EQN. 50)

γ_((ac,f,min)) ≦LP _(max)(U* _((ac,f)) ΔV)≦γ_((ac,f,max))   (EQN. 51)

where LP_(min)(U*ΔV) and LP_(max)(U*ΔV) are the minimized and maximizedresults of the linear programming problem. In other words, if aparticular element of the result of the minimization of U*ΔV subject tothe constraints of ΔV_(min) and a ΔV_(max) is greater than or equal tothe γ_((ac,f,min)) and the result of the maximization of U*ΔV subject tothe constraints of ΔV_(min) and a ΔV_(max) is less than or equal to theγ_((ac,f,max)) for that particular element and that particular fault“f”, then it should be considered a zero. In this manne, the measurementerror of the detector may be taken into account and still be used withthe methods detailed above.

Referring back to the discussion of FIG. 2, recall that in a step 206the circuit was broken down into electrically isolated groups ofcomponents called “clusters.” By definition, a cluster has at least onenode that is inaccessible and has no more than one path joining it toany other cluster. Also recall that these clusters were further brokendown in a step 208 by generating smaller “reduced” clusters that containfewer components and require fewer accessible nodes to test therebyreducing the computational complexity and execution time necessary togenerate the equivalence classes, U* matrices and test limits. To helpreduce the size of clusters, several rules are typically followed whengenerating clusters.

The first rule deals with open or apparent open devices. In a preferredembodiment, the test voltages applied by the test hardware are smallenough that active devices such as transistors and integrated circuitswill not be activated. As such, these active devices can be modeledeither in terms of their parasitic effects, or as open or apparent opendevices. Therefore, most integrated circuit devices, not-placed jumpers,open switches, and connectors are considered to be open devices. If adevice does exhibit an intrinsic impedance, or has significant parasiticeffects, it should not be considered as an open device and should bedescribed in the circuit topology. An example of this would be aone-shot integrated circuit that has a built-in resistance betweencertain pins. Other apparent open devices include active devices such astransistors, diodes, zener diodes, and enhancement mode field effecttransistors (FETs). Open and apparent open devices will be removed fromthe circuit topology during the cluster generation process since theywill not contribute to the cluster topology. When these devices areremoved, it helps decrease the size of clusters because there are fewerconnections in the circuit.

To prevent active devices from being activated, the applied testvoltages should always be kept less than the 0.7 volts typicallyrequired to forward bias a P-N junction of a silicon diode. Testvoltages that are less than or equal to 0.2 volts are even moredesirable. Circuits having active devices fabricated from othermaterials, such as gallium arsenide, or based on other technologies,such as vacuum tubes or both, may have different turn-on voltages andthe maximum test voltage can be adjusted accordingly.

The second rule deals with shorted or apparent shorted devices. Shorteddevices include fuses, jumpers, and closed switches. Apparent shorteddevices include devices that have an impedance that is below a thresholdvalue. This threshold value may be set as an environment variable, orspecified by some other form of input to the test generation program. Ina preferred embodiment, the default threshold considers any device withan impedance of less than one ohm to be an apparent short.

The third rule deals with variable devices. These typically includevariable resistors, potentiometers, variable capacitors, and variableinductors. These devices cannot be attached to an inaccessible node, butthey can be part of a cluster topology. In an embodiment of the presentinvention, if a variable device is connected to an inaccessible node, anerror condition is generated and the user notified that anotheraccessible node is necessary to test the variable device.

FIG. 12 is a flowchart illustrating a process for generating clustersfrom a board topology consistent with an embodiment of the presentinvention. The process starts generating a cluster from the existingboard topology in a step 1202. In a step 1204, the process selects astarting device by searching the board topology for a device that isconnected to one accessible node and one inaccessible node. In a step1206, a topological representation is created. A topologicalrepresentation represents devices, nodes, and their interconnection. Ina preferred embodiment, this topological representation is a topologygraph.

The topology graph is created by adding the starting device to an emptyundirected graph. Devices are added to the topology graph as edges andthe nodes connected to that device are added to the topology graph asvertices. The devices connected to the starting devices are added to thegraph. Then the devices connected to those devices are added and so onuntil all devices that can trace a path to the starting device have beenadded. As devices are being added to the graph, the first rule statedabove is followed so that open or apparent open devices are not added tothe graph. This has the effect of “removing” open or apparent opendevices from the board and cluster topology. Also, dangling componentsare not added to the cluster topology. This has the effect of removingdangling components from the cluster topology.

Once constructed, the graph is reduced according to the second rulestated above in a step 1208. In a step 1208, edges associated with shortand apparent short devices are removed from the graph and the verticesof these devices are combined into a single vertex. This effectivelyremoves any devices shorted out by these devices from the clustertopology.

In a step 1210, the topology graph is recursively traversed startingwith the inaccessible node connected to the starting device. As thegraph is being traversed, the recursion stops at accessible nodes, butcontinues descending edges (devices) connected to inaccessible nodes. Inthis manner, all of the accessible nodes that are reachable along a pathfrom the starting inaccessible node through only inaccessible nodes aretraversed. In a step 1212, the accessible nodes that were stopped at areplaced in a list of possible test points.

The principles used to construct and traverse the topology graph (andthe other graphs constructed and traversed in succeeding steps and inFIG. 13) may be used with different topological representations anddifferent traversal methods. These representations and traversal methodsare known in the art.

In a step 1214, all the paths between any two nodes in the list ofpossible test points are found. In a step 1216, all the devices alongthese paths are marked as being in the cluster being generated. In astep 1218, any accessible nodes along or at the end of the paths foundin step 1214 that are not already in the list of possible test pointsare added to the list of possible test points.

In a step 1220, inaccessible nodes that have not previously beentraversed in step 1210 or step 1220 (un-traversed nodes) are used as astarting point for traversing the topology graph. The topology graph isrecursively traversed starting with the un-traversed inaccessible node.As the graph is being traversed, the recursion stops at accessiblenodes, but continues descending edges (devices) connected toinaccessible nodes. In this manner, all of the accessible nodes that arereachable along a path from the starting un-traversed inaccessible nodethrough only inaccessible nodes are traversed. In a step 1222, theaccessible nodes that were stopped at are placed in the list of possibletest points if they were not already there.

If any nodes were added to the list of possible test points in steps1218 or step 1220, the process loops back to step 1214. If no nodes wereadded to the list of possible test points, the process moves to step1226 where all the devices marked as being in the cluster beinggenerated are stored and then removed from the board topology. In a step1228, if there are no devices left in the board topology, then theprocess terminates in a step 1230. If there are still devices left inthe board topology, then the process loops back to step 1202 to startgenerating a new cluster from the devices left in the board topology.

In FIG. 2 in a step 208, each cluster is further broken down intoreduced clusters. A reduced cluster is a group of targeted componentsthat reduces the size and complexity of the test generation,measurement, and fault analysis problems in succeeding stages of thetest process. FIG. 13 is a flowchart that illustrates the process forgenerating reduced clusters from a cluster consistent with an embodimentof the present invention.

In a step 1302, a topology graph containing the devices in the clusterbeing broken into reduced clusters is generated. For this discussion,this will be called the cluster topology graph. To construct the clustertopology graph the process begins with an empty undirected graph. Eachdevice in the cluster is added to the cluster topology graph one at atime. Each device is added to the cluster topology graph as an edge andthe nodes connected to that device are added to the topology graph asvertices if not already in the cluster topology graph.

The process for generating an individual reduced cluster begins in astep 1304. In a step 1306, an inaccessible node is selected from thecluster topology graph. In a step 1308, the cluster topology graph isrecursively traversed starting with the selected inaccessible node. Asthe cluster topology graph is being traversed, the recursion stops ataccessible nodes, but continues descending edges (devices) connected toinaccessible nodes. In this manner, all of the accessible nodes that arereachable along a path from the starting inaccessible node and throughonly inaccessible nodes, are traversed.

In a step 1310, the devices traversed in step 1308 are placed in thecurrent reduced cluster. In a step 1311, the accessible nodes stopped atin step 1308 are placed in a list of potential stimulus nodes for thisreduced cluster. In a step 1312, these devices are stored as a reducedcluster and the list of potential stimulus nodes for this reducedcluster are stored.

In a step 1314, the devices (edges) of the reduced cluster are deletedfrom the cluster topology graph. In a step 1316, any nodes (vertices)that are no longer connected to any devices are removed from the clustertopology graph. In a step 1318, if there are still inaccessible nodesleft in the cluster topology graph, the process loops back to step 1304to begin generating another reduced cluster. If there are noinaccessible nodes left in the cluster topology graph, the processterminates in a step 1320. Any devices left in the cluster topologygraph when the process terminates in step 1320 may be tested byconventional in-circuit test techniques.

Several of the steps in FIG. 12 and FIG. 13 refer to recursivelytraversing a graph. To illustrate in an exemplary manner this function,examine the exemplary undirected topology graph represented in FIG. 16consistent with an embodiment of the present invention. The vertexes ofthe graph represented in FIG. 16 correspond to nodes. The node number isshown inside of each circle. Accessible nodes are shown with an asterisk(*) next to the node number. The rest of the nodes are inaccessible. Theedges of the graph represented in FIG. 16 are shown as lines. Each edgecorresponds to a component (or branch). The name of the component isshown next to the line. Note that the topology graph in FIG. 16corresponds to the circuit shown in FIG. 4 except with differentaccessible nodes.

Say that the traversal starts at node 2. Node 2 is an inaccessible nodeso all of the branches (or edges) connected to node 2 need to berecursively traversed. This means that edges R₄ and R₃ are to betraversed. Which one is traversed first does not matter. If R₄ istraversed first, it leads to node 4. Since node 4 is also inaccessible,all the branches connected to node 4 except the branch followed to getto node 4 need to be recursively traversed. Accordingly, R₅ is thentraversed leading to node 0.

Since node 0 is an accessible node, the traversal stops at this node anddoes not traverse down the branches connected to node 0. The traversalthen backs up to node 4. At node 4, all of the branches connected tonode 4 have already been traversed (i.e. R₅) so the traversal backs backup to node 2. At node 2, R₄ has already been traversed, but node 3 hasnot. Therefore, the traversal continues down R₃ to node 3.

At node 3, R₂ and R₁ have not been traversed. Once again, which one istraversed first does not matter. Accordingly, R₂ is arbitrarily chosenfirst for this example. When R₂ is traversed, it leads to node 0. Onceagain, since node 0 is an accessible node the branches connected to node0 are not traversed. Note that to avoid an infinite loop, traversal alsostops at nodes that have been previously traversed.

After the traversal backs up to node 3, it traverses down branch R₁ tonode 1. Node 1 is an accessible node so the branches connected to node 1are not traversed. The traversal then returns to node 3. At node 3, thetraversal backs up to node 2 because all of the branches connected tonode 3 have been traversed. All of the branches connected to node 2, thestarting node, have been recursively traversed. Since all of thebranches connected to the starting node have now been traversed, theprocess ends,

Accordingly, in the above example, the devices traversed are R₁, R₂, R₃,R₄, and R₅. J₁ was not traversed, and the accessible nodes stopped atare nodes 0 and 1.

It may not be necessary to measure all of the accessible nodes connectedto devices in the whole cluster to test the components in a reducedcluster. Accordingly, for each reduced cluster, access pruning isperformed to determine a minimized set of accessible nodes that need tobe measured. By reducing the number of nodes measured, the size of theU* matrices, etc. are also reduced. This simplifies the computationsnecessary to determine if a fault has occurred and speeds test time.

FIG. 14 is a flowchart illustrating the steps taken to perform nodepruning for a reduced cluster consistent with an embodiment of thepresent invention. In a step 1402, the devices in the reduced clusterare divided into equivalence classes. This is typically done by firstcreating a Z matrix for the entire cluster. This Z matrix has rows thatcorrespond to each of the accessible nodes connected to devices in thecluster. The columns correspond to the devices in cluster. Theequivalence classes are then produced by checking the columns, andgroups of columns, that correspond to the devices in the reduced clusterfor independence. This follows the basic procedure for generatingequivalence classes described, above, except that only combination ofcolumns corresponding to devices in the reduced cluster are checked.

In a step 1404, the nodes in the list of potential stimulus nodes thatwere generated when the reduced cluster was generated are placed in thelist of test points for this reduced cluster. These nodes are alsoeliminated from later consideration. That means that these nodes willnot be eliminated in later steps as test points. In a step 1406, a nodestill under consideration for elimination is selected. This node is anaccessible node since only accessible nodes can be possible test points.

In a step 1408, a check is made to see if the selected node can beeliminated as a test point without changing the equivalence classes ofthe reduced cluster. This check is accomplished by first removing therow corresponding to the selected node from the Z matrix used in step1402. Then, this stripped Z matrix is used to divide the devices of thereduced cluster into equivalence classes. If there is a difference inthe equivalence classes just produced and those produced in step 1402,then this node is needed as a test point for this reduced cluster andthe process proceeds to step 1412. If there was no difference, this nodeis not needed as a test point for this reduced cluster and the processproceeds to step 1410.

In a step 1412, a node that is needed as a test point is placed in thelist of test points for this reduced cluster. The node is alsoeliminated from further consideration so it will not be selected again.The process then proceeds to step 1414. In a step 1410, a node that isnot needed as a test point is eliminated from further consideration soit will not be selected again for possible placement in the list of testpoints for this reduced cluster.

In a step 1414, if there are still accessible nodes under consideration,the process loops back to step 1406. If all of the accessible nodes havebeen tested to see if they should become test points, the processproceeds to step 1416. In a step 1416, the list of test points for thisreduced cluster is stored. Then the process terminates in a step 1418.In a preferred embodiment, only the accessible nodes in the list of testpoints will be used to generate the U* matrices and test limits for theequivalence classes of this reduced cluster.

Although several specific embodiment of the invention has been describedand illustrated, the invention is not to be limited to the specificforms, arrangements, and steps so described and illustrated. Forexample, many of the stimuli shown in the specific embodiments are shownas current sources. However, these could also be voltage sources. Theinvention is limited only by the claims.

What is claimed is:
 1. A method of selecting and testing targetedcomponents, comprising: (a) constructing a topological representation ofa first group of components, wherein said first group of components areinterconnected by a first set of nodes and wherein said first set ofnodes being divided into a first subset of nodes that are inaccessibleand a second subset of nodes that are accessible; (b) selecting a firstinaccessible node from said first subset of nodes; (c) traversing saidtopological representation starting with said first inaccessible node,said traversing stopping at members of said second subset of nodes andsaid traversing continuing to traverse at members of said first subsetof nodes; (d) selecting a first set of targeted components, wherein saidtargeted components are members of said first group of components thatwere traversed in step (c); (e) applying a stimulus to a selectedstimulus location, wherein said selected stimulus location is a memberof said second subset of nodes; and (f) measuring a set of responses tosaid stimulus-applied at said selected stimulus location, wherein saidset of responses are measured at a first member of said second subset ofnodes.
 2. The method of claim 1 further comprising: (g) eliminating saidfirst set of targeted components from said topological representation.3. The method of claim 2 further comprising: (h) eliminating a thirdsubset of nodes from said first subset of nodes wherein said thirdsubset of nodes have no connections in said topological representation.4. The method of claim 3 further comprising: i selecting a second set oftargeted components if said first subset of nodes is not empty.
 5. Themethod of claim 4, wherein step i is accomplished by repeating steps(b), (c), and (d).
 6. The method of claim 1 wherein after constructingthe topological representation further comprising: eliminating a thirdsubset of nodes from said first subset of nodes wherein said thirdsubset of nodes have no connections in said topological representation.7. The method of claim 6 further comprising: selecting a second set oftargeted components if said first subset of nodes is not empty.
 8. Themethod of claim 7, wherein selecting the second set of targetedcomponents if said fist subset of nodes is not empty is accomplished byrepeating steps (b), (c), and (d).
 9. A program storage medium readableby a computer, tangibly embodying a program of instructions executableby the computer to perform method steps for selecting and testingtargeted components, said method comprising: (a) constructing atopological representation of a first group of components, wherein saidfirst group of components are interconnected by a first set of nodes andwherein said first set of nodes being divided into a first subset ofnodes that are inaccessible and a second subset of nodes that areaccessible; (b) selecting a first inaccessible node from said firstsubset of nodes; (c) traversing said topological representation startingwith said first inaccessible node, said traversing stopping at membersof said second subset of nodes and said traversing continuing totraverse at members of said first subset of nodes; (d) selecting a firstset of targeted components, wherein said targeted components are membersof said first group of components that were traversed in step (c); (e)applying a stimulus to a selected stimulus location, wherein saidselected stimulus location is a member of said second subset of nodes;and (f) measuring a set of responses to said stimulus applied at saidselected stimulus location, wherein said set of responses are measuredat a first member of said second subset of nodes.
 10. The programstorage medium of claim 9 further comprising: (g) eliminating said firstset of targeted components from said topological representation.
 11. Theprogram storage medium of claim 10 further comprising: (h) eliminating athird subset of nodes from said first subset of nodes wherein said thirdsubset of nodes have no connections in said topological representation.12. The program storage medium of claim 11 further comprising: iselecting a second set of targeted components if said first subset ofnodes is not empty.
 13. The program storage medium of claim 12, whereinstep i is accomplished by repeating steps (b), (c), and (d).
 14. Theprogram storage medium of claim 10 further comprising: h selecting asecond set of targeted components if said first subset of nodes is notempty.
 15. The program storage medium of claim 14, wherein step h isaccomplished by repeating steps (b), (c), and (d).
 16. The programstorage medium of claim 9 wherein after constructing the topologicalrepresentation further comprising: eliminating a third subset of nodesfrom said first subset of nodes wherein said third subset of nodes haveno connections in said topological representation.
 17. An apparatus forselecting and testing targeted components, comprising: (a) means forconstructing a topological representation of a first group ofcomponents, wherein said first group of components are interconnected bya first set of nodes and wherein said first set of nodes being dividedinto a first subset of nodes that are inaccessible and a second subsetof nodes that are accessible; (b) means for selecting a firstinaccessible node from said first subset of nodes; (c) means fortraversing said topological representation starting with said firstinaccessible node, said traversing stopping at members of said secondsubset of nodes and said traversing continuing to traverse at members ofsaid first subset of nodes; (d) means for selecting a first set oftargeted components, wherein said targeted components are members ofsaid first group of components that were traversed in step (c); (e)means for applying a stimulus to a selected stimulus location, whereinsaid selected stimulus location is a member of subset of nodes; and (f)means for measuring a set of responses to said stimulus applied at saidselected stimulus location, wherein said set of responses are measuredat a first member of said second subset of nodes.
 18. The apparatus ofclaim 17 further comprising: means for eliminating said first set oftargeted components from said topological representation.
 19. Theapparatus of claim 18 further comprising: means for eliminating a thirdsubset of nodes from said first subset of nodes wherein said thirdsubset of nodes have no connections in said topological representation.20. The apparatus of claim 19 further comprising: means for selecting asecond set of targeted components if said first subset of nodes is notempty.
 21. The apparatus of claim 17 further comprising: means foreliminating a third subset of nodes from said first subset of nodeswherein said third subset of nodes have no connections in saidtopological representation.
 22. The apparatus of claim 21 furthercomprising: means for selecting a second set of targeted components ifsaid first subset of nodes is not empty.
 23. An apparatus for selectingand testing targeted components, comprising: (a) a topologicalrepresentation generator for constructing a topological representationof a first group of components, wherein said first group of componentsare interconnected by a first set of nodes and wherein said first set ofnodes being divided into a first subset of nodes that are inaccessibleand a second subset of nodes that are accessible; (b) a node selectorthat selects a first inaccessible node from said first subset of nodes;(c) a traverser that traverses said topological representation startingwith said first inaccessible node, said traversing stopping at membersof said second subset of nodes and said traversing continuing totraverse at members of said first subset of nodes; (d) a componentselector that selects a first set of targeted components, wherein saidtargeted components are members of said first group of components thatwere traversed in step (c); (e) a stimulator that applies a stimulus toa selected stimulus location, wherein said selected stimulus location isa member of said second subset of nodes; and (f) a measurer thatmeasures a set of responses to said stimulus applied at said selectedstimulus location, and wherein said set of responses are measured at afirst member of said second subset of nodes.
 24. The apparatus of claim23 further comprising: a component eliminator that eliminates said firstset of targeted components from said topological representation.
 25. Theapparatus of claim 24 further comprising: a node eliminator thateliminates a third subset of nodes from said first subset of nodeswherein said third subset of nodes have no connections in saidtopological representation.
 26. The apparatus of claim 25 wherein saidcomponent selector selects a second set of targeted components if saidfirst subset of nodes is not empty.
 27. The apparatus of claim 23further comprising: a node eliminator that eliminates a third subset ofnodes from said first subset of nodes wherein said third subset of nodeshave no connections in said topological representation.
 28. Theapparatus of claim 27 wherein said component selector selects a secondset of targeted components if said first subset of nodes is not empty.29. An apparatus for testing, comprising: a processing system, saidprocessing system comprising at least one processor, wherein saidprocessing system constructs a topological representation of a firstgroup of components, said first group of components being interconnectedby a first set of nodes and said first set of nodes being divided into afirst subset of nodes that are inaccessible and a second subset of nodesthat are accessible, and wherein said processing system selects a firstinaccessible node from said first subset of nodes, and wherein saidprocessing system traverses said topological representation startingwith said first inaccessible node, said traversing stopping at membersof said second subset of nodes and said traversing continuing totraverse at members of said first subset of nodes, and said processingsystem selects a first set of targeted components, wherein said targetedcomponents are members of said first group of components that weretraversed by said processing system; a stimulator system that applies astimulus to a selected stimulus location, wherein said selected stimuluslocation is a member of said second subset of nodes; and, a measuringsystem, wherein said measuring system measures a set of responses tosaid stimulus applied at said selected stimulus location, and whereinsaid set of responses are measured at a first member of said secondsubset of nodes.
 30. The apparatus of claim 29 wherein said processingsystem also eliminates said first set of targeted components from saidtopological representation.
 31. The apparatus of claim 30 wherein saidprocessing system eliminates a third subset of nodes from said firstsubset of nodes wherein said third subset of nodes have no connectionsin said topological representation.
 32. The apparatus of claim 31wherein said processing system selects a second set of targetedcomponents if said first subset of nodes is not empty.
 33. The apparatusof claim 30 wherein said processing system eliminates a third subset ofnodes from said first subset of nodes wherein said third subset of nodeshave no connections in said topological representation.
 34. Theapparatus of claim 33 wherein said processing system selects a secondset of targeted components if said first subset of nodes is not empty.